Policy packages as potential routes to urban road pricing in the UK

This paper focuses on urban road pricing as a demand management policy that is often regarded as radical and generally unacceptable. Road pricing often gets delayed or abandoned due to low acceptability. This may be due to the fact that complex interactions and drivers of change affect road transport management and require cooperation within implementation networks. The implementation network is a group of people (referred to as partners and actors) who co-ordinate the introduction of policy tools. The drivers of change include any internal or external influences that have an effect on the time, place, or ‘shape’ of the policy measures being introduced. Demand management measures that focus on 'sustainable transport' usually address a limited set of objectives and are often implemented alone i.e. are not necessarily combined with other policy measures. When combined with other measures, it is not always clear whether the multiple interactions between policy tools and implementation networks have been sufficiently considered. Examples of ongoing implementation of policy package in the UK are the support of road pricing initiatives combined with public transport improvements by the Transport Innovation Fund. The objectives of the paper are twofold. First, we present a review of the UK urban road pricing situation. Second, we contrast the emerging issues against six key implementation factors. The analysis of three existing UK road pricing examples - London, Edinburgh and Durham – shows the importance of combining policy tools. Furthermore, through the above examples and theoretical arguments, we emphasise the additional need of creating and maintaining strong networks when implementing policy packages.

Engineering design knowledge management in de-centralised virtual enterprises

The problems of collaborative engineering design and knowledge management at the conceptual stage in a network of dissimilar enterprises was investigated. This issue in engineering design is a result of the supply chain and virtual enterprise (VE) oriented industry that demands faster time to market and accurate cost/manufacturing analysis from conception. The solution consisted of a de-centralised super-peer net architecture to establish and maintain communications between enterprises in a VE. In the solution outlined below, the enterprises are able to share knowledge in a common format and nomenclature via the building-block shareable super-ontology that can be tailored on a project by project basis, whilst maintaining the common nomenclature of the ‘super-ontology’ eliminating knowledge interpretation issues. The two-tier architecture layout of the solution glues together the peer-peer and super-ontologies to form a coherent system for both internal and virtual enterprise knowledge management and product development.

Enhancing the Global Ocean Observing System to meet evidence based needs for the ecosystem-based management of coastal ecosystem services

Ecosystem-based approaches (EBAs) to managing anthropogenic pressures on ecosystems, adapting to changes in ecosystem states (indicators of ecosystem health), and mitigating the impacts of state changes on ecosystem services are needed for sustainable development. EBAs are informed by integrated ecosystem assessments (IEAs) that must be compiled and updated frequently for EBAs to be effective. Frequently updated IEAs depend on the sustained provision of data and information on pressures, state changes, and impacts of state changes on services. Nowhere is this truer than in the coastal zone, where people and ecosystem services are concentrated and where anthropogenic pressures converge. This study identifies the essential indicator variables required for the sustained provision of frequently updated IEAs, and offers an approach to establishing a global network of coastal observations within the framework of the Global Ocean Observing System. The need for and challenges of capacity-building are highlighted, and examples are given of current programmes that could contribute to the implementation of a coastal ocean observing system of systems on a global scale. This illustrates the need for new approaches to ocean governance that can achieve coordinated integration of existing programmes and technologies as a first step towards this goal.

Channel Catchments Cluster. Recent developments in tools and techniques for water quality management in the France (Channel) England region

The Channel Catchments Cluster (3C) aims to capitalise on outputs from some of the recent projects funded through the INTERREG IVa France (Channel) England programme. The river catchment basins draining into the Channel region drain an area of 137,000km2 and support a human population of over 19M. Throughout history, these catchments, rivers and estuaries have been centres of habitation, developed through commerce and industry, providing transport links to hinterland areas. These catchments also provide drinking water and food through provision of agriculture, fisheries and aquaculture. In addition, many parts of the region are also economically important now for the tourism and leisure industries. Consequently, there is a need to manage the balance of these many and varied human activities within the catchments, rivers, estuaries and marine areas to ensure that they are maintained or restored to good environmental condition . This document highlights some of the recent work carried out by projects within the INTERREG IVa programme that provide tools and techniques to assist in the achievement of these goals.

The importance of independent marine evidence provision in a multi-use environment: MBA information resources and The Marine Life Information Network.

One of the most pressing challenges today is the need to manage our oceans on a sustainable basis, balancing opportunities for exploitation with the need for conservation and protection. A vital tool for informing sustainable management is access to accurate, up-to-date marine environmental data and information, which is also seen as ‘independent’ by industry, conservationists, policy-makers and other Stakeholders. The Marine Biological Association has specialised in providing independent evidence for over a century and hosts a number of programmes dedicated to independent evidence provision. For example, the Marine Life Information Network (MarLIN) is the most comprehensive information resource for the marine environment of the British Isles and also the largest review of the effects of human activities and natural events on marine species and habitats ever undertaken. MarLIN, along with the Data Archive for Seabed Species and Habitats (DASSH and other MBA information resources, is currently being used to support a wide range of UK and European legislation as well as providing vital underpinning information for industry (e.g. through informing EIAs). We provide an overview of MarLIN in particular whilst examining the importance of ‘independent’ scientific information in a multi-use environment.

Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey–predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries.

Sports programs of the Spanish radio in the social network Facebook: compared study (2010-2015)

Social networks have increasingly become a showcase where the media can be promoted. Like many other media, radio stations have made use of social networks to promote themselves in a better way and, sometimes, to keep more feedback with their listeners. But not all programs make the same use and not all of them have managed to reach in the same way his followers. This article discusses the consolidation in the social networks of the major radio sports programs in Spain. Through a comparative analysis between 2010 and 2015, throughout the text, the authors have tried to observe the evolution of the programs and, at the same time, to establish comparisons between the followers that these programs have on social networks and the number of listeners as EGM.

Towards hierarchical management of autonomic components: a case study

We address the issue of autonomic management in hierarchical component-based distributed systems. The long term aim is to provide a modelling framework for autonomic management in which QoS goals can be defined, plans for system adaptation described and proofs of achievement of goals by (sequences of) adaptations furnished. Here we present an early step on this path. We restrict our focus to skeleton-based systems in order to exploit their well-defined structure. The autonomic cycle is described using the Orc system orchestration language while the plans are presented as structural modifications together with associated costs and benefits. A case study is presented to illustrate the interaction of managers to maintain QoS goals for throughput under varying conditions of resource availability.

Modelling Patient Duration of Stay to Facilitate Resource Management of Geriatric Hospitals

A fundamental aspect of health care management is the effective allocation of resources. This is of particular importance in geriatric hospitals where elderly patients tend to have more complex needs. Hospital managers would benefit immensely if they had advance knowledge of patient duration of stay in hospital. Managers could assess the costs of patient care and make allowances for these in their budget. In this paper, we tackle this important problem via a model which predicts the duration of stay distribution of patients in hospital. The approach uses phase-type distributions conditioned on a Bayesian belief network.

Classifying Network Protocols: A ‘2 Way’ Flow Approach

The identification and classification of network traffic and protocols is a vital step in many quality of service and security systems. Traffic classification strategies must evolve, alongside the protocols utilising the Internet, to overcome the use of ephemeral or masquerading port numbers and transport layer encryption. This research expands the concept of using machine learning on the initial statistics of flow of packets to determine its underlying protocol. Recognising the need for efficient training/retraining of a classifier and the requirement for fast classification, the authors investigate a new application of k-means clustering referred to as 'two-way' classification. The 'two-way' classification uniquely analyses a bidirectional flow as two unidirectional flows and is shown, through experiments on real network traffic, to improve classification accuracy by as much as 18% when measured against similar proposals. It achieves this accuracy while generating fewer clusters, that is, fewer comparisons are needed to classify a flow. A 'two-way' classification offers a new way to improve accuracy and efficiency of machine learning statistical classifiers while still maintaining the fast training times associated with the k-means.